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Cloud computing is an emerging computing paradigm with a large collection of heterogeneous autonomous systems with flexible computational architecture. Task scheduling is an important step to improve the overall performance of the cloud computing. Task scheduling is also essential to reduce power consumption and improve the profit of service providers by reducing processing time. This paper focuses on task scheduling using a multi-objective nested Particle Swarm Optimization(TSPSO) to optimize energy and processing time. The result obtained by TSPSO was simulated by an open source cloud platform (CloudSim). Finally, the results were compared to existing scheduling algorithms and found that the proposed algorithm (TSPSO) provide an optimal balance results for multiple objectives.

Background:The therapeutic potential of EcA is limited due to immunogenicity and short half-life in patients. Results: Several EcA variants were constructed that showed markedly reduced immunogenicity and cytotoxicity against leukemic lymphoblasts. Conclusion: Small changes in subunit interface and B-cell epitope significantly reduced immunogenicity and enhanced cytotoxicity. Significance: These variants have promising potential in the advanced asparaginase therapy of leukemia.

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