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Service-Oriented and Cloud Computing: 5th IFIP WG 2.14 by Marco Aiello, Einar Broch Johnsen, Schahram Dustdar, Ilche

By Marco Aiello, Einar Broch Johnsen, Schahram Dustdar, Ilche Georgievski

This booklet constitutes the refereed court cases of the fifth eu convention on Service-Oriented and Cloud Computing, ESOCC 2016, held in Vienna, Austria, in September 2016.
The sixteen complete papers offered have been rigorously reviewed and chosen from 33 submissions. The papers are equipped in topical sections on guidelines and function, edition, SLA-aware companies, task placement, compositionality, and fault tolerance.

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Additional info for Service-Oriented and Cloud Computing: 5th IFIP WG 2.14 European Conference, ESOCC 2016, Vienna, Austria, September 5-7, 2016, Proceedings

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We compare three different approaches: Static SLAs, in which SLAs do not change during their lifetime; Renegotiation, in which the parties can renegotiate the existing SLA; and our approach, Dynamic SLAs, which enables the definition of modifications in the agreements. For this comparison, we analyse the number of violations, penalty and the total revenue of the provider. The need for dynamism in this use case is demonstrated in three cases: – Request for modification from the consumers, commonly caused by change of requirements, which we simulated by randomly selecting services in execution; Table 4.

The need to have access to VMs and permission to run tailor-made foreign codes is neither acceptable not practical in most general cases. Secondly, many techniques aim to predict throughput of an application only to avoid contention by using/controlling one resource type (CPU, Mem, or Disk). Finally, most approaches target known applications that do not have multidimensional resource demands: they are pure CPU, Mem, or Disk intensive. To address these shortcomings, we designed vmBBThrPred to directly model and formulate throughput of an unknown application/VM according to its resource usages.

1 Accuracy of vmBBThrPred Table 5 shows different prediction accuracy for different benchmarks: ranging from 76 %–99 % for the Blind (ThrB) and 94 %–100 % for the Assisted (ThrA) mode. For CPU intensive applications (marked as (*/–/–)), the accuracy of Table 5. 02 acc. 02 acc. latency. latency. latency. latency. latency. numwrite. latency. numwrite. latest vmBBThrPred 31 vmBBThrPred were significantly high (>94 %). Accuracy of ThrA/B for disk intensive applications ((–/L/L) and (–/*/H)) were also noticeably high with the minimum accuracy of 82 % and 94 % for ThrB and ThrA, respectively.

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