Exemplary CF consisting of 5 clouds connected by network.

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... services. On the other hand, the management of CF is more complex comparing to this which is required for a standalone cloud. So, the effective management of resources and services in CF is the key point for getting additional profit from such system. CF is the system composing of a number of clouds connected by a network, as it is illustrated on Fig. 1. The main concept of CF is to operate as one computing system with resources distributed among particular clouds. In this chapter we present a multi-level model for traffic management in CF. Each level deals with specific class of algorithms, which should together provide satisfactory service of the clients, while maintaining optimal ...

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... workflow in Fig. 10 consists of four abstract tasks, and each task maps to three concrete services (alternatives), which are deployed by (independent) third-party service providers. For each task T i there are M i concrete service providers CS (i,1) , . . . , CS (i,Mi) available that implement the functionality corresponding to task T i . For each request ...

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... workflow in Fig. 10 consists of four abstract tasks, and each task maps to three concrete services (alternatives), which are deployed by (independent) third-party service providers. For each task T i there are M i concrete service providers CS (i,1) , . . . , CS (i,Mi) available that implement the functionality corresponding to task T i . ...

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... for all requests that are not processed within δ p a penalty V had to be paid. After the execution of a single task within the workflow, the orchestrator decides on the next concrete service to be executed, and composite service provider pays to the third party provider per single invocation. The decision points for given tasks are illustrated at Fig. 10 by A, B, C and D. The decision taken is based on (1) execution costs, and (2) the remaining time to meet the end-to-end deadline. The response time of each concrete service provider CS (i,j) is represented by the random variable D (i,j) . After each decision the observed response time is used for updating the response time distribution ...

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... tests we are able to identify if an significant change occurred and the policy has to be recalculated. Our approach is based on fully dynamic, run-time service selection and composition, taking into account the responsetime commitments from service providers and information from response-time realizations. We illustrate our approach using Fig. 11. The execution starts with an initial lookup table at step (1). This could be derived from initial measurements on the system. After each execution of a request in step (2) the empirical distribution is updated at step (3). A DP based lookup table could leave out unattractive concrete service providers. In that case we do not receive ...

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... If for example, in Fig. 10, the second alternative of the third task has not been used in the last ten requests, the probe timer for alternative two has value U (3,2) = 10. After a probe we immediately update the corresponding distribution. No test is applied here as probes are collected less frequent compared to processed ...

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... RAM. Figure 12 shows the scores a VM achieves on the Apache and PyBench benchmark and the RAM it utilizes depending on the VRAM. For each VRAM configuration 10 measurements are conducted. ...

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... each VRAM configuration 10 measurements are conducted. Figure 12a shows that when the VM executes Apache, it never utilizes more than 390 MB of RAM. In particular, for a VM with 100 to 350 MB of VRAM the amount of RAM that is maximally utilized continuously increases but does not further increase, when more than 350 MB of VRAM are added. ...

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... VRAM configuration 10 measurements are conducted. Figure 12a shows that when the VM executes Apache, it never utilizes more than 390 MB of RAM. In particular, for a VM with 100 to 350 MB of VRAM the amount of RAM that is maximally utilized continuously increases but does not further increase, when more than 350 MB of VRAM are added. Therefore, Fig. 12a shows that a VM with less than 350 MB of VRAM utilizes all RAM that is available, which seems to imply, that this amount of RAM is critical for performance. However, Fig. 12a also depicts that the Apache score only increases for up to 250 MB of VRAM and that this increase is marginal compared to the increase of RAM that is utilized. ...

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... 100 to 350 MB of VRAM the amount of RAM that is maximally utilized continuously increases but does not further increase, when more than 350 MB of VRAM are added. Therefore, Fig. 12a shows that a VM with less than 350 MB of VRAM utilizes all RAM that is available, which seems to imply, that this amount of RAM is critical for performance. However, Fig. 12a also depicts that the Apache score only increases for up to 250 MB of VRAM and that this increase is marginal compared to the increase of RAM that is utilized. Therefore, the dependency between VRAM and utilized RAM is much stronger than the dependency between VRAM/utilized RAM and Apache score. In particular, while the RAM utilization ...

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... is particularly interesting, because this configuration range includes 100 MB of VRAM which constrains the VM's RAM utilization to less than half of what the VM alone (without executing any workload) would utilize. Figure 12b shows that when the VM executes PyBench, the VM process utilizes 270 MB of RAM at most. Although the VM is constraint in its RAM utilization, when it has less than 250 MB of VRAM, there is no correlation between the achieved PyBench score and the VM's VRAM, as the PyBench score does not increase. ...

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... Fig. 12 shows that RAM, which is actively utilized by a VM (be it on startup or when executing an application), not necessarily impacts the VM's performance. In particular, even if the RAM utilized by a VM varies from 100 MB to 350 MB, the VM's Apache score, i.e., its ability to sustain concurrent server requests, only changed by 10%. For ...

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... RAM Utilization. The 7zip benchmark reveals an interesting dependency of VCPUs and RAM utilization (cf. Fig. 13). As Fig. 13a shows, for one to three VCPUs a VM executing the 7zip benchmark utilizes 1 GB of RAM and for every two additional cores the RAM utilization increases by 400 MB (the VM had 9 GB of ...

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... RAM Utilization. The 7zip benchmark reveals an interesting dependency of VCPUs and RAM utilization (cf. Fig. 13). As Fig. 13a shows, for one to three VCPUs a VM executing the 7zip benchmark utilizes 1 GB of RAM and for every two additional cores the RAM utilization increases by 400 MB (the VM had 9 GB of ...

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... distinct pattern in which RAM is utilized gives reason to believe, that it is essential for performance. Therefore, Fig. 13b compares the 7zip scores achieved by VMs with 1 and 9 GB of VRAM. As Fig. 13a shows, the more VCPUs a VM has, the more it will be constrained by only having 1 GB of VRAM, while 9 GB of VRAM not even constrain a VM with 24 VCPUs. In line with this observation, Fig. 13b shows that the difference between the 7zip scores achieved by VMs ...

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... distinct pattern in which RAM is utilized gives reason to believe, that it is essential for performance. Therefore, Fig. 13b compares the 7zip scores achieved by VMs with 1 and 9 GB of VRAM. As Fig. 13a shows, the more VCPUs a VM has, the more it will be constrained by only having 1 GB of VRAM, while 9 GB of VRAM not even constrain a VM with 24 VCPUs. In line with this observation, Fig. 13b shows that the difference between the 7zip scores achieved by VMs with 1 and 9 GB of VRAM grows with the number of VCPUs. However, the score ...

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... gives reason to believe, that it is essential for performance. Therefore, Fig. 13b compares the 7zip scores achieved by VMs with 1 and 9 GB of VRAM. As Fig. 13a shows, the more VCPUs a VM has, the more it will be constrained by only having 1 GB of VRAM, while 9 GB of VRAM not even constrain a VM with 24 VCPUs. In line with this observation, Fig. 13b shows that the difference between the 7zip scores achieved by VMs with 1 and 9 GB of VRAM grows with the number of VCPUs. However, the score difference is rather moderate compared to the large difference in terms of RAM utilization. In particular, a VM with 24 VCPUs utilizes more than 5 GB of RAM, if available. This is five times as ...

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... the 7zip scores achieved by these VMs only differ by 15%. Figure 14a plots the Apache scores achieved by a VM with 1 to 9 VCPUs, whereat 16 measurements per configuration were conducted. The figure shows that the best performance is achieved, when the VM has three or four VCPUs, while additional VCPUs linearly decrease the Apache score. ...

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... effect, which is termed multi-core-penalty occurred, independent of whether VCPUs were pinned to physical CPUs. Figure 14a also demonstrates that, while three VCPUs perform best for an unstressed host, two VCPUs perform best, when the host is stressed. Furthermore, the multi-corepenalty does not occur, when the benchmark is executed natively, i.e., directly on the host and not inside a VM. ...

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... example, for the Apache benchmark it was found that for 9 VCPUs the utilized CPU time is roughly twice as high as the CPU time utilized by one to three VCPUs (although the Apache score was significantly lower for 9 VCPUs). Figure 14b shows that the multi-core penalty also occurs for the aio-stress benchmark, where a VM with one VCPU constantly achieves a higher aio-stress score than any VM with more VCPUs. In particular, the aio-stress score of a VM with only one VCPU is on average a 30% higher than the aio-stress score of VMs with more VCPUs. ...