Multi-tenancy Support Research Articles

BeeFlow: Behavior tree-based Serverless workflow modeling and scheduling for resource-constrained edge clusters

Serverless computing has gained popularity in edge computing due to its flexible features, including the pay-per-use pricing model, auto-scaling capabilities, and multi-tenancy support. Complex Serverless-based applications typically rely on Serverless workflows (also known as Serverless function orchestration) to express task execution logic, and numerous application- and system-level optimization techniques have been developed for Serverless workflow scheduling. However, there has been limited exploration of optimizing Serverless workflow scheduling in edge computing systems, particularly in high-density, resource-constrained environments such as system-on-chip clusters and single-board-computer clusters. In this work, we discover that existing Serverless workflow scheduling techniques typically assume models with limited expressiveness and cause significant resource contention. To address these issues, we propose modeling Serverless workflows using behavior trees, a novel and fundamentally different approach from existing directed-acyclic-graph- and state machine-based models. Behavior tree-based modeling allows for easy analysis without compromising workflow expressiveness. We further present observations derived from the inherent tree structure of behavior trees for contention-free function collections and awareness of exact and empirical concurrent function invocations. Based on these observations, we introduce BeeFlow, a behavior tree-based Serverless workflow system tailored for resource-constrained edge clusters. Experimental results demonstrate that BeeFlow achieves up to 3.2× speedup in a high-density, resource-constrained edge testbed and 2.5× speedup in a high-profile cloud testbed, compared with the state-of-the-art. BeeFlow also demonstrates superior robustness in scenarios with heavy system workloads.

Traffic scheduling, network slicing and virtualization based on deep reinforcement learning

The revolutionary paradigm of the 5 G network slicing introduces promising market possibilities through multi-tenancy support. Customized slices might be provided to other tenants at a different price as an emerging company to operators. Network slicing is difficult to deliver higher performance and cost-effective facilities through render resources utilisation in alignment with customer activity. Therefore, this paper, Deep Reinforcement Learning-based Traffic Scheduling Model (DRLTSM), has been proposed to interact with the environment by searching for new alternative actions and reinforcement patterns believed to encourage outcomes. The DRL for network slicing situations addresses power control and core network slicing and priority-based sizing involves radio resource. This paper aims to develop three main network slicing blocks i) traffic analysis and network slice forecasting, (ii) network slice admission management decisions, and (iii) adaptive load prediction corrections based on calculated deviations; Our findings suggest very significant possible improvements show that DRLTSM is dramatically improving its efficiency rate to 97.32%, scalability and compatibility in comparison with its baseline.

A Scalable Monitoring Framework for Network Slicing in 5G and Beyond Mobile Networks

An efficient and scalable monitoring system is a critical component for any network to monitor and validate the functioning of the running services and the underlying infrastructure. This is more valid in 5G, as it relies on the network slicing concept, which adds many challenges to the monitoring system. Besides data isolation and multi-tenancy support, network slices require monitoring different types of resources, like RAN, computing, memory, network data rate, which belong to different technological domains, managed by different entities. Moreover, the monitoring system needs to be scalable, as in 5G a high-number of running network slices is envisioned. In this paper, we devise a novel monitoring framework for network slicing ready mobile networks, which features: 1) scalable monitoring system that supports a high number of running network slices in parallel; 2) technological domain agnostic thanks to a novel data collection (or monitoring) communication protocol; 3) support of multi-tenancy in a cloud-native environment. The framework has been implemented in a 5G facility, and its performance has been extensively evaluated.

On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration

Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network. MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks. This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies. It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options. In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties. Finally, this paper overviews the current standardization activities and elaborates further on open research challenges.

About Cloud Forensics: Challenges and Solutions

Cloud computing is gaining wide popularity due to low infrastructure cost and high resource utilization. Cloud can be private or public. Users can store their data and avail cloud sessions on pay-per-use basis. This multi-tenancy support can lead to several problems. Cloud forensics is a cross discipline of cloud computing and digital forensics. It ensures to find out the culprit when there is a security breach. Several techniques are proposed to secure user data and provide legally acceptable evidences in case of cyber crime. This chapter aims at discussing the issues and challenges of cloud forensics and the currently available solutions. Logs are widely accepted as legal evidence of how fraud was perpetrated. Creation and maintenance of logs also poses a number of challenges. This chapter also aims at exploring these challenges and solutions for managing logs.

A Secure Multi-Tenant Model for SaaS System

World Wide Web is working as a fuel to the systems like SaaS. SaaS shares both the application delivery model and the business model. It provides the customers to access the application using a web browser. it provides better scalability to users. SaaS[2] applications mostly support multitenant system to provide the service to many customers at a single instance. It provides users to access the database application server in authentication environment, but it has been found that it is scalable but not much secure for all the customers accessing the same functionalities, it may occur to the information hacked system. So in this paper I am proposing one approach to handle the multi-tenant environment and secure system with the privacy preservation model approach for the multi-tenant support SaaS system to increase the security and scalability. General Terms Security, SaaS in Cloud, Emerging Web technologies.

Enhanced Monitoring-as-a-Service for Effective Cloud Management

This paper introduces the concept of monitoring-as-a-service (MaaS), its main components, and a suite of key functional requirements of MaaS in cloud. We argue that MaaS should support not only the conventional state monitoring capabilities, such as instantaneous violation detection, periodical state monitoring, and single tenant monitoring, but also performance-enhanced functionalities that can optimize on monitoring cost, scalability, and the effectiveness of monitoring service consolidation and isolation. In this paper, we present three enhanced MaaS capabilities and show that window-based state monitoring is not only more resilient to noises and outliers, but also saves considerable communication cost. Similarly, violation-likelihood-based state monitoring can dynamically adjust monitoring intensity based on the likelihood of detecting important events, leading to significant gain in monitoring service consolidation. Finally, multitenancy support in state monitoring allows multiple cloud users to enjoy MaaS with improved performance and efficiency at more affordable cost. We perform extensive experiments in an emulated cloud environment with real-world system and network traces. The experimental results suggest that our MaaS framework achieves significant lower monitoring cost, higher scalability, and better multitenancy performance.