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Cost-Aware Resource Management for Federated Clouds Using Resource Sharing Contracts

  • June 2017
DOI:10.1109/CLOUD.2017.38
  • Conference: IEEE Cloud 2017
Authors:
Jinlai Xu at ByteDance
Jinlai Xu
  • ByteDance
Balaji Palanisamy
Balaji Palanisamy
Balaji Palanisamy
  • This person is not on ResearchGate, or hasn't claimed this research yet.
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Abstract

Cloud computing and its pay-as-you-go model continue to provide significant cost benefits and a seamless service delivery model for cloud consumers. The evolution of small-scale and large-scale geo-distributed datacenters operated and managed by individual cloud service providers raises new challenges in terms of effective global resource sharing and management of autonomously-controlled individual datacenter resources. Earlier solutions for geo-distributed clouds have focused primarily on achieving global efficiency in resource sharing that results in significant inefficiencies in local resource allocation for individual datacenters leading to unfairness in revenue and profit earned. In this paper, we propose a new contracts-based resource sharing model for federated geo-distributed clouds that allows cloud service providers to establish resource sharing contracts with individual datacenters apriori for defined time intervals during a 24 hour time period. Based on the established contracts, individual cloud service providers employ a cost-aware job scheduling and provisioning algorithm that enables tasks to complete and meet their response time requirements. The proposed techniques are evaluated through extensive experiments using realistic workloads and the results demonstrate the effectiveness, scalability and resource sharing efficiency of the proposed model.
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Cost-aware Resource Management
for Federated Clouds
Using Resource Sharing Contracts
Jinlai Xu, Balaji Palanisamy
School of Information Sciences
University of Pittsburgh
Cloud Computing
Data
analytics
App Media Health
care
CPU Storage Database Network Management
Cloud
2
IoT
Problems of previous “standalone” clouds
•Resources available in a single data center are limited
•One datacenter can cover most but not all the peak workloads
•Under provisioning will cause heavy penalties
Lost revenue
Lost users
Resources
Demand
Capacity
Time (days)
12 3
Resources
Demand
Capacity
Time (days)
12 3
Resources
Demand
Capacity
Time (days)
12 3
Slide Credits: Berkeley RAD Lab 3
Problems of previous “standalone” clouds
•Resources available in a single data center are limited
•Dynamic electricity price has both pros and cons
•The fluctuation is significant during days (from 10s to 100s)
•The fluctuation is significant also in one day (up to 6x in one day)
•High electricity price will cost datacenters a lot
•So there is a need for the datacenters to share resources with each
other to both increase the potential capacity and decrease the cost.
Data from National Grid 4
Resource sharing mechanisms
•Virtual Geo-distributed Cluster
•One Cloud Service Provider(CSP)
manages several geo-distributed
datacenters and makes them work
together
•Federated Cloud
•Several CSPs get together to build
a federation to share resources in
the geo-distributed scenario
5
Previous solutions
Virtual geo-distributed cluster Federated cloud
6
WAN
Seattle
Berkeley Beijing
London
Broker
The weaknesses of previous solutions
•Global controlling
•Either the information of the datacenters are aggregated to a centralized
controller to help allocate the resource
•Or all the requests from the users are submitted to a centralized broker to
respond.
•Global optimization
•Global optimization does not mean individual optimized.
•The profit of each datacenter is not guaranteed which loses fairness
7
Contracts-based federated cloud
•Resource sharing contract
•Stipulate the rights and duties
between the buyer and seller
•Effect time
•Price
•Resource amount
•…
•Problem 1: How to build the
resource sharing contracts?
•Problem 2: How to appropriately
schedule the jobs based on the
contracts?
8
Contract
Contract
Contract
Contract
CSP 1 CSP 2
CSP 3 CSP 4
Problem1: Contracts establishment
•The auction mechanisms fit the properties of the problem well
•Both competing and cooperating need to be considered
•The essence behind the auction is to match the demands and supplies to
allocate some resources.
•Properties we desire in the auction mechanism:
•Double auction: both buyers and sellers bid in the auction
•Truthfulness: bidders tend to bid with their true valuation
•Budget balance: auctioneer will not subsidize in the auction
•McAfee mechanism satisfies the above criteria
9
Existing double
auction designs Truthfulness Ex-post Budget
Balance
Individual
Rationality
Average X√ √
VCG √X √
McAfee √√√
Proposed bidding strategies
•The utility function of each CSP basically contains two parts:
•The charges from the customers by running the tasks
•The operating cost of the infrastructure
•Mixed strategies for the potential buyer:
•Lack of resource: bid by the charge value
•Otherwise: bid by the operating cost
•One strategy for the potential seller:
•Idle resource: bid by the operating cost
•Other strategies can be considered by adding into the utility function
10
Winning bids decision
•CSPs bid by the strategies
•Order the sell bids with ascending
order and buy bids with descending
order
•Find the break even index
•Calculate the intermediate price
•If    or    
•Choose the winning bids (first   1bids
win)
•Calculate the clear prices (buy price and
sell price respectively)
•If    
•Choose the winning bids (first bids win)
•Calculate the clear prices (buy price and
sell price are equal to )
11
$10
$20
$40
$20
$10
$40
Sell Bids Buy Bids
CSP 1 CSP 2 CSP 3
Break
Even
Index
1
2
3
= 2
Calculate:
Intermediate price:
= +
2=25
=20    20 =
Sell price ==$20
Buy price ==$20
Winning
Bids
Contracts Establishment Process
•Two layers of loop
For each time slot   
For each type-resource
CSPs bid;
Winning bids decision;
Continue to next type of resource;
Continue to next time slot;
•So for each time slot and each type
of resource, there is an auction and
an array of auction result.
•The contract is built by the array of
the auction results one by one.
12
$10
$20
$40
$20
$10
$40
Sell Bids Buy Bids
CSP 1 CSP 2 CSP 3
Winning
Bids
Effective time: in
Resource type:
Sell price =$20
Buy price =$20
Problem2: Scheduling
•Cost-aware mechanism
•Sort the contracts by their unit buy price (price per unit resource)
•Separate the contracts to two sets:
•Lower-cost contracts (price less than the local operation cost)
•Higher-cost contracts (price higher than the local operation cost)
•Schedule the jobs:
•Fill the lower-cost contract
•Fill the local resource
•Use the higher-cost contract only when the above two kinds of resource are
exhausted
13
Scheduling
Illustration
•First in First out (FIFO)
•Cost-aware
14
Time →
Servers
Job Queue
Scheduler
11:00 12:00
Two
Sides
CSP1
->CSP2
Time
11:00~12:00
Type
2 (2 servers)
Price
$20
Head
Tail
Jobs
$10
Contract
Cost-aware:
Unit Price > Local Operation Cost
Place the job to local servers first
Cost: $5/server
Contract
Unit Price Local Resource
Simulation setup –CSPs and datacenters
Locations from AWS EC2’s data 15
# CSP (each with
one datacenter) 25
# servers per DC 600
PUE 1.2
Max response
time (s) 600
Default setting
Simulation setup –server
•IBM server x3550
•CPU: 2 x [Xeon X5675 3067 MHz, 6 cores],
•Memory: 16GB
Load 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Power
(Watt) 58.4 98 109 118 128 140 153 170 189 205 222
Picture and spec from IBM 16
Simulation setup -dataset
•Real workload trace from Google
•Replay the trace for every task
•Start time
•Length
•Resource requirement(CPU, memory)
•Dynamic electricity price dataset
from National Grid
•Randomly choose one day’s
electricity price array for each
datacenter
17
Simulation setup –compared mechanisms
•NF: each datacenter runs alone without federation
•ConBLF: Local resource First Contracts-Based
•ConBCA: Cost-Aware Contracts-Based
•RT: real-time complete cooperation by a broker
18
Experiment Result –impact of # of servers
•The electricity cost is optimized with using the contracts-based
mechanisms.
•The success rate of contracts-based mechanisms are between RT and
NF
19
0
0.2
0.4
0.6
0.8
1
1.2
200 400 600 800 1000
NORMALIZED ELECTRICITY COST
/ SUCCESS TASK
# SERVERS / DATACENTER
NF ConBLF ConBCA RT
0
0.2
0.4
0.6
0.8
1
1.2
200 400 600 800 1000
SUCCESS RATE
# SERVERS / DATACENTER
NF ConBLF ConBCA RT
Experiment Result –impact of # of CSPs
•The result is similar to the scenario of impact of number of servers
per datacenter
20
0
0.2
0.4
0.6
0.8
1
1.2
10 20 30 40 50
NORMALIZED ELECTRICITY COST
/ SUCCESS TASK
# OF CSP
NF ConBLF ConBCA RT
0
0.2
0.4
0.6
0.8
1
1.2
10 20 30 40 50
AVERAGE UTILIZATION
# OF CSP
NF ConBLF ConBCA RT
Experiment Result –impact of prediction error
•Prediction errors do not significantly influence the electricity cost and
success rate
•Performance of the contracts-based mechanisms is not significantly
influenced by the prediction error
21
0
0.2
0.4
0.6
0.8
1
1.2
12345
NORMALIZED ELECTRICITY COST
/ SUCCESS TASK
PREDICTION ERROR (%)
NF LFConB CAConB RT
0
0.2
0.4
0.6
0.8
1
1.2
12345
SUCCESS RATE
PREDICTION ERROR (%)
NF LFConB CAConB RT
Experiment result -fairness
22
•RT represents large variance
•All the contracts-based mechanisms represent low variance
•7/25 CSPs lose profits after participating into the federated cloud
when using RT mechanism
•Contracts-based mechanisms perform better
-5
0
5
10
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
NORMALIZED PROFIT
CSP #
NF LFConB CAConB ConAConB ConACAConB RT
Conclusion
•Propose a contracts-based mechanism for resource sharing between CSPs
(cloud service providers) in a federated cloud.
•Develop an auction-based mechanism for contract establishment and a suit
of contracts-aware and cost-aware scheduling techniques that maximize
the local profits of the CSPs while servicing the individual job requirements.
•Evaluate the performance of the proposed approach using a trace-driven
simulation study with realistic workload traces and electricity pricing.
•The contracts-based solution achieves good performance and performs
significantly better than the traditional model especially in fairness
measurement.
23
Thanks
24
Backup slides
Jinlai Xu, Balaji Palanisamy
School of Information Sciences
University of Pittsburgh
System model
•Cloud Service Provider
•Resource management
•Provisioning and scheduling
•Contracts managing
•Manage the contracts
•Observe the statuses
•Workload estimating
•Predict the workload and help in
establishing the contracts
26
Tasks and provision requests
Servers
User layer
Physical layer
Virtual layer
Routers & Switches
Infrastructure
Cloud OS
Provision &
Scheduler
subnet
subnet
Service
Delivery Service layer
Contracts
Effective Time
Resource amount
Price
Contracts
manager
Cloud OS
subnet
contractor
Resource
manager
Workload
Estimator
System model
•Federation Coordinator
•Consulting
•Match the demands and supplies of
the CSPs
•Contract building
•Establish the resource sharing
contracts based on the consulting
result
27
Coordinator
Consult Contract
Builder
CSP 1 CSP 2 …
Demand &
Supply
Contracts
Resource type: dedicated cloud
•Emerging IaaS service
•IBM Bluemix dedicated
•AWS EC2 dedicated instances
•Physically isolated hardware
•Firm performance Guarantee
•More configurable
•More security
28
Proof of truthfulness for McAfee mechanism
•If    or    
•The first 1buyers and sellers have no incentive to change their
declaration since this will have no effect on their price;
•The  buyer and seller have no incentive to change since they don't trade
anyway, and if they do enter the trading (e.g. increases his declaration
above  ), their profit from trading will be negative.
•If    
•The first buyers and sellers have no incentive to change their declaration
since this will have no effect on their price;
•The (+ 1) buyer and seller have no incentive to change since they don't
trade anyway, and if they do enter the trading (e.g.  increases his
declaration above ), their profit from trading will be negative.
29
Algorithms pseudocode
30
Simulator
•Implement in JAVA
•5000+ lines of code
•80+ classes
31
Experiment Result –impact of # of servers
•The average utilization of the servers are better than RT and NF
32
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
200 400 600 800 1000
AVERAGE SERVER UTILIZATION
# SERVERS / DATACENTER
NF ConBLF ConBCA RT
Experiment Result –impact of # of CSPs
•The result is similar to the scenario of impact of number of servers
per datacenter
33
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
10 20 30 40 50
SUCCESS RATE
# OF CSP
NF LFConB CAConB RT
Experiment Result –impact of prediction error
•Prediction errors do not significantly influence the server utilization
34
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
12345
AVERAGE SERVER UTILIZATION
PREDICTION ERROR (%)
NF LFConB CAConB RT
Experiment result –impact of contract interval
•The electricity cost is increased a little(2% to 5%) with increasing the
interval of the contracts (the length for one time slot)
35
0
0.2
0.4
0.6
0.8
1
1.2
1200 2400 3600 4800 6000 7200
NORMALIZED ELECTRICITY COST
/ SUCCESS TASK
CONTRACT INTERVAL (S)
NF LFConB CAConB RT
0
0.2
0.4
0.6
0.8
1
1.2
1200 2400 3600 4800 6000 7200
SUCCESS RATE
CONTRACT INTERVAL (S)
NF LFConB CAConB RT
Experiment result –impact of contract interval
•The contract interval length does not significantly influence the server
utilization
36
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1200 2400 3600 4800 6000 7200
AVERAGE SERVER UTILIZATION
CONTRACT INTERVAL (S)
NF LFConB CAConB RT

Supplementary resource (1)

JinlaiXu-Contracts-Cloud2017
Data
July 2017
Jinlai Xu · Balaji Palanisamy
... In Habibi et al. [17], similar to [16], cloud providers send their request to the federated broker, and then the federated broker distributes received requests among federation members based on different objective functions. Xu and Palanisamy [18] proposed a contracts-based resource sharing model for geo-distributed federated clouds. The proposed model permits service providers to settle resource sharing contracts and apply a cost-aware job scheduling for pre-defined time intervals during 24 h of a day. ...
... Nevertheless, Bayesian Ridge Regression can be used when a long history of workload is available to train a robust model. The proposed model is compared with Samaan et al. [13], Xu et al. [18] and Rebai et al. [33] ...
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Article
  • Jan 2021
Due to the problems exist in non-geographic federated clouds, the geographic ones are considered. Nevertheless, the approaches that have already been proposed to allocate resources across the geographical federated clouds have two basic problems that we will address in this article: (1) Lack of proper distribution of user requests leading to increases file transfer volume and cost, as well as response time to user requests, (2) Lack of appropriate resource sharing among requests due to: (1) the use of a centralized DC and (2) considering the satisfaction of single objective which case (1) suffers the problem of single-point of failure and case (2) raises an obstacle for the situations need considering multi conflicting objectives. Concerning the problem of one, it should be said that as federal DCs are distributed globally in the geographic clouds, the cost of file transfer between DCs in these clouds is more focused than the concentrated ones. Since there has been no work in this field in the geo-distributed federated clouds, we have presented a new scheduling mechanism based on hypervolume for the distribution of applications that leads to increasing service quality and reducing file transfer cost. Concerning the problem of two, the previous solutions in the geographic federated clouds have focused on a centralized resource sharing with single objective (increase of the cloud service provider (CSP) profit). These solutions not only just consider the CSP profitability, but, because of the possibility of failure of central broker of resource-sharing, suffer the single-point of failure. In this paper, we propose a new, autonomic and peer-to-peer multi-objective resource sharing approach that considers objectives: (1) enhancing the CSP's profit, (2) decreasing the network latency and (3) decreasing file transfer traffic and (3) increasing fairness in CSPs' profit. The techniques presented in this paper are evaluated by extensive experiments using real workloads. To validate the proposed method, we have extended the CloudSim tool. The results of our experiments show the increase of performance in the scheduling and resource-sharing objectives among which the main objectives of average rate of success, profit and execution time were enhanced 8.5%, 15.47% and 25.84%, respectively compared with previous studies.
Contract-Based Resource Sharing for Time Effective Task Scheduling in Fog-Cloud Environment
Article
  • Mar 2020
Fog computing as an extension of the cloud based infrastructure, provides a better computing platform than cloud computing for mobile computing, Internet of Things, etc. One of the problems is how to make full use of the resources of the fog so that more requests of applications can be executed on the edge, reducing the pressure on the network and ensuring the time requirement of tasks. The high mobility of fog nodes also has a great impact on the task completion time and user satisfaction. Thus, a general IoT-Fog-Cloud computing architecture with a contract-based resource sharing mechanism is proposed in this paper. The contract establishment problem of resource sharing mechanism among fog clusters is modeled as a sealed-bid bilateral auction in order to take full advantage of the fog resources and ensure that more tasks could be executed on the fog. Then, we propose a scheduling method based on functional domain construction to mitigate the influence of mobility of fog nodes. It includes the selection of critical fog nodes and the construction of fog function domains based on spectral clustering. The selection of critical fog nodes is used to find the best fog nodes in each fog cluster with respect to the betweenness centrality, computing performance and communication delay to the IoT nodes. The critical nodes are responsible for building the functional domains of the remaining fog nodes in each fog cluster. Functional domain construction is used to determine the set of fog nodes contained in the corresponding functional domain. Finally, through extensive simulation experiments, the performance difference between the proposed method and the other four methods in terms of average service time, average utilization of fog nodes, success rate of tasks, average WLAN delay and the average cost of successful tasks are evaluated. Results show that our method generally outperforms the other four methods in these metrics.
Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers
Article
Big data: The next frontier for innovation, competition, and productivity
Technical Report
  • May 2011
Location-aware associated data placement for geo-distributed data-intensive applications
Conference Paper
  • Apr 2015
Volley: Automated data placement for geo-distributed cloud services
Article
  • Jan 2010
3-D Data Management: Controlling Data Volume, Velocity, and Variety
Article
  • Jan 2001
Workload Prediction Using ARIMA Model and Its Impact on Cloud Applications' QoS
Article
  • Aug 2014
As companies shift from desktop applications to Cloud-based Software as a Service (SaaS) applications deployed on public Clouds, the competition for end-users by Cloud providers offering similar services grows. In order to survive in such a competitive market, Cloud-based companies must achieve good Quality of Service (QoS) for their users, or risk losing their customers to competitors. However, meeting the QoS with a cost-effective amount of resources is challenging because workloads experience variation over time. This problem can be solved with proactive dynamic provisioning of resources, which can estimate the future need of applications in terms of resources and allocate them in advance, releasing them once they are not required. In this paper, we present the realization of a Cloud workload prediction module for SaaS providers based on the Autoregressive Integrated Moving Average (ARIMA) model. We introduce the prediction based on the ARIMA model and evaluate its accuracy of future workload prediction using real traces of requests to web servers.We also evaluate the impact of the achieved accuracy in terms of efficiency in resource utilization and QoS. Simulation results show that our model is able to achieve an average accuracy of up to 91%, which leads to efficiency in resource utilization with minimal impact on the QoS.
Customer Satisfaction-Aware Scheduling for Utility Maximization on Geo-distributed Cloud Data Centers
Article
With the increasingly growing amount of service requests from the world-wide customers, the cloud systems are capable of providing services while meeting the customers' satisfaction. Recently, to achieve the better reliability and performance, the cloud systems have been largely depending on the geographically distributed data centers. Nevertheless, the dollar cost of service placement by service providers (SP) differ from the multiple regions. Accordingly, it is crucial to design a request dispatching and resource allocation algorithm to maximize net profit. The existing algorithms are either built upon energy-efficient schemes alone, or multi-type requests and customer satisfaction oblivious. They cannot be applied to multi-type requests and customer satisfaction-aware algorithm design with the objective of maximizing net profit. This paper proposes an ant-colony optimization-based algorithm for maximizing SP's net profit (AMP) on geographically distributed data centers with the consideration of customer satisfaction. First, using model of customer satisfaction, we formulate the utility (or net profit) maximization issue as an optimization problem under the constraints of customer satisfaction and data centers. Second, we analyze the complexity of the optimal requests dispatchment problem and rigidly prove that it is an NP-complete problem. Third, to evaluate the proposed algorithm, we have conducted the comprehensive simulation and compared with the other state-of-the-art algorithms. Also, we extend our work to consider the data center's power usage effectiveness. It has been shown that AMP maximizes SP net profit by dispatching service requests to the proper data centers and generating the appropriate amount of virtual machines to meet customer satisfaction. Moreover, we also demonstrate the effectiveness of our approach when it accommodates the impacts of dynamically arrived heavy workload, various evaporation rate and consideration of power usage effectiveness. Copyright © 2014 John Wiley & Sons, Ltd.
Carbon-Aware Load Balancing for Geo-distributed Cloud Services
Conference Paper
  • Aug 2013
Recently, data center carbon emission has become an emerging concern for the cloud service providers. Previous works are limited on cutting down the power consumption of the data centers to defuse such a concern. In this paper, we show how the spatial and temporal variabilities of the electricity carbon footprint can be fully exploited to further green the cloud running on top of geographically distributed data centers. We jointly consider the electricity cost, service level agreement (SLA) requirement, and emission reduction budget. To navigate such a three-way tradeoff, we take advantage of Lyapunov optimization techniques to design and analyze a carbon-aware control framework, which makes online decisions on geographical load balancing, capacity right-sizing, and server speed scaling. Results from rigorous mathematical analyses and real-world trace-driven empirical evaluation demonstrate its effectiveness in both minimizing electricity cost and reducing carbon emission.
Joint Request Mapping and Response Routing for Geo-distributed Cloud Services
Conference Paper
  • Apr 2013
Many cloud services are running on geographically distributed datacenters for better reliability and performance. We consider the emerging problem of joint request mapping and response routing with distributed datacenters in this paper. We formulate the problem as a general workload management optimization. A utility function is used to capture various performance goals, and the location diversity of electricity and bandwidth costs are realistically modeled. To solve the large-scale optimization, we develop a distributed algorithm based on the alternating direction method of multipliers (ADMM). Following a decomposition-coordination approach, our algorithm allows for a parallel implementation in a datacenter where each server solves a small sub-problem. The solutions are coordinated to find an optimal solution to the global problem. Our algorithm converges to near optimum within tens of iterations, and is insensitive to step sizes. We empirically evaluate our algorithm based on real-world workload traces and latency measurements, and demonstrate its effectiveness compared to conventional methods.
Intelligent virtual machine placement for cost efficiency in geo-distributed cloud systems
Conference Paper
  • Jun 2013
An important challenge of running large-scale cloud services in a geo-distributed cloud system is to minimize the overall operating cost. The operating cost of such a system includes two major components: electricity cost and wide-area-network (WAN) communication cost. While the WAN communication cost is minimized when all virtual machines (VMs) are placed in one datacenter, the high workload at one location requires extra power for cooling facility and results in worse power usage effectiveness (PUE). In this paper, we develop a model to capture the intrinsic trade-off between electricity and WAN communication costs, and formulate the optimal VM placement problem, which is NP-hard due to its binary and quadratic nature. While exhaustive search is not feasible for large-scale scenarios, heuristics which only minimize one of the two cost terms yield less optimized results. We propose a cost-aware two-phase metaheuristic algorithm, Cut-and-Search, that approximates the best trade-off point between the two cost terms. We evaluate Cut-and-Search by simulating it over multiple cloud service patterns. The results show that the operating cost has great potential of improvement via optimal VM placement. Cut-and-Search achieves a highly optimized trade-off point within reasonable computation time, and outperforms random placement by 50%, and the partial-optimizing heuristics by 10-20%.