Distributed streaming platforms such as Pravega, Kafka, and Pulsar are widely used for high-throughput, low-latency data processing. As these platforms increasingly handle sensitive data, ensuring data confidentiality and integrity becomes critical. Trusted Execution Environments (TEEs) offer secure computations that can be used on client-side processing, but their impact on performance must be carefully assessed. This study evaluates the write latency of Pravega clients running in TEEs compared to those in standard (non-secured) environments. We found that under typical workloads, TEE-based clients experience approximately 50% higher latency due to the overhead of secure executions. However, when data rates exceed 976 MB/s, the Pravega broker reaches its throughput limit, causing latency to spike for standard clients. In contrast, TEE-based clients exhibit more stable latency under these high-throughput conditions. These findings can be helpful for data architects, as systems highlight a trade-off: while latency may increase, the impact could be acceptable in certain scenarios given the enhanced security benefits.

Cloud computing is a computing platform that allows users to access various kinds of computing services over the internet. Cloud provides on-demand, scalable and highly available resources on pay-per-usage subscriptions. Cloud is an optimum solution for executing a large number of different size tasks as for the computing capability it offers. Task scheduling is one of the major open challenges that need to be addressed. The Task scheduling problem in the cloud is known to be an NP- complete problem. Hence heuristics can be used to get an optimal solution. There have been many heuristics proposed for the task scheduling problem in the cloud. None of them has considered the total execution time of the virtual machine as a factor for finding a better schedule. In this paper, we proposed a new task scheduling algorithm named Total Resource Execution Time Aware Algorithm (TRETA) which takes into account the total execution time of computing resources in obtaining an optimal schedule. The algorithm is compared with Min-Min, Min-Max, FCFS, and MCT heuristics for Makespan, Degree of Imbalance and System Throughput. The proposed algorithm shows a significant amount of improvement in Makespan compared to other heuristics. The algorithm also outperforms other heuristics with respect to System Throughput and Degree of Imbalance which results in better workload distribution among the cloud resources.

Optimizing energy efficient Virtual Machine Consolidation (VMC) in a cloud computing environment, which is a non-linear multi-objective NP-hard problem, plays a vital role in decreasing energy consumption, and increasing Quality of Service (QoS). In this paper, VMC is formulated as a multi-objective optimization problem, which has three conflicting objectives, power consumption, Service Level Agreements Violation (SLAV) and Mean Time Before Host Shutdown (MTBHS). We propose a multi-objective optimization algorithm based on Multi-Objective Sine Cosine Algorithm (MOSCA) for the VMC. We evaluate the performance of our model by applying two multi-objective algorithms, namely, Multi-Objective Evolutionary Algorithm based on Decomposition (MOEAD) and Non-dominated Sorting Genetic Algorithm (NSGAII). Our research mainly focus on two tasks, i.e.,evaluating and comparing the multi-objective algorithms to find out the optimal solution and develop a MOSCA based algorithm to solve the proposed VMC model. The simulation results illustrated that the propose multi-objective model meets the optimal solutions amongst the three conflicting objectives, which significantly reduces the power consumption, SLAV and maximize the MTBHS. It got the best performance according to the Multi-objective Optimization Problem (MOP) indicators.