Optimizing Application Scheduling and Resource Allocation in the Computing Continuum: A Comprehensive Survey

Core Concepts

This survey reviews the state-of-the-art methods for scheduling and placing distributed applications on the Computing Continuum, with a focus on optimizing key non-functional objectives such as completion time, energy consumption, and economic cost.

Abstract

This survey examines the research works on scheduling and placement of distributed applications on the Computing Continuum, which encompasses Cloud, Fog, and Edge computing infrastructures. The key findings are:

Completion Time Optimization:

Many methods focus on minimizing the application completion time, considering factors like processing time, network latency, and bandwidth.
Techniques include multi-layer resource partitioning, integer programming, reinforcement learning, and heuristic-based scheduling.

Energy Consumption Optimization:

Some works aim to reduce the energy consumption of application execution, using approaches like linear regression, decentralized optimization, and genetic algorithms.
The methods manage resource utilization and consolidation to minimize energy usage.

Economic Cost Optimization:

A subset of the research explores optimizing the economic cost of application execution, balancing factors like service pricing, provider profit, and user satisfaction.
Techniques include price-based competition, multi-objective optimization, and game theory-based scheduling.

Evaluation Approaches:

The majority of the works use simulation tools like CloudSim, iFogSim, and FogTorchPI to evaluate their methods.
A smaller fraction also employs real-world testbeds for validation.

The survey highlights that while significant progress has been made in optimizing application completion time, the research on energy efficiency and economic cost optimization remains limited. Future work should focus on developing comprehensive methods that jointly optimize these critical non-functional objectives for the Computing Continuum.

Stats

"Google approximately consumes 15.5 TW h of electricity annually, with a significant portion being attributed to its data centers."
"41% of the research articles focus on optimizing application completion time, 29.5% focus on energy consumption, and 29.5% focus on economic cost."
"33% of the research articles use non-linear or linear programming, 37% use heuristic methods, 7% use game theory, 10% use graph theory, and 13% use machine learning."
"46% of the research articles use simulation, 42% use real testbeds, and 11% use both simulation and real testbeds for evaluation."

Quotes

"The challenge of executing applications with different requirements on heterogeneous devices requires effective methods for solving NP-hard resource allocation and application scheduling problems."
"To underscore the magnitude of this issue, Google, for instance, approximately consumes 15.5 TW h of electricity annually, with a significant portion being attributed to its data centers."
"The unification of Cloud, Fog, and Edge infrastructures (aka. Computing Continuum) aims to provide a seamless aggregation of distributed resources of all kinds to support the emerging dataflow processing applications."

Key Insights Distilled From

Scheduling of Distributed Applications on the Computing Continuum: A Survey

by Narges Mehra... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00005.pdf

Deeper Inquiries

How can the scheduling and placement methods be extended to handle dynamic and unpredictable workloads in the Computing Continuum?

In order to handle dynamic and unpredictable workloads in the Computing Continuum, scheduling and placement methods can be extended through several approaches:

Dynamic Resource Allocation: Implementing dynamic resource allocation techniques that can adjust resource provisioning based on workload variations. This can involve real-time monitoring of resource usage and workload patterns to make on-the-fly adjustments.

Machine Learning Models: Utilizing machine learning models to predict workload fluctuations and optimize resource allocation accordingly. These models can learn from historical data and adapt to changing workload patterns.

Autoscaling: Implementing autoscaling mechanisms that can automatically scale resources up or down based on workload demands. This ensures that the system can handle sudden spikes in workload without manual intervention.

Task Migration: Developing algorithms for task migration between different computing nodes to balance the workload and optimize resource utilization. This can involve migrating tasks from overloaded nodes to underutilized ones.

Fault Tolerance: Incorporating fault tolerance mechanisms to handle failures in the system caused by unpredictable workloads. This can involve replicating tasks or data across multiple nodes to ensure continuity of service.

By incorporating these strategies, scheduling and placement methods can become more adaptive and resilient to dynamic and unpredictable workloads in the Computing Continuum.

What are the potential challenges and trade-offs in developing a unified optimization framework that jointly considers completion time, energy consumption, and economic cost?

Developing a unified optimization framework that considers completion time, energy consumption, and economic cost poses several challenges and trade-offs:

Complexity: Integrating multiple objectives into a unified framework increases the complexity of the optimization problem. Balancing these objectives while ensuring optimal solutions can be challenging.

Trade-offs: There may be trade-offs between completion time, energy consumption, and economic cost. For example, reducing completion time may increase energy consumption or cost. Finding the right balance among these factors is crucial.

Resource Constraints: Limited resources in the Computing Continuum can pose constraints on optimization. Allocating resources to minimize completion time while optimizing energy consumption and cost requires careful resource management.

Algorithm Efficiency: Developing algorithms that can efficiently optimize multiple objectives simultaneously is a technical challenge. The computational complexity of solving such multi-objective optimization problems can be high.

Data Accuracy: The accuracy of data used for optimization, such as workload predictions, energy consumption models, and cost estimations, can impact the effectiveness of the unified framework. Ensuring data accuracy is essential for reliable optimization.

Addressing these challenges and trade-offs requires a comprehensive understanding of the system dynamics and careful consideration of the interplay between completion time, energy consumption, and economic cost in the optimization process.

How can the scheduling algorithms leverage emerging technologies like edge intelligence and federated learning to enhance decision-making and resource utilization in the Computing Continuum?

Scheduling algorithms can leverage emerging technologies like edge intelligence and federated learning to enhance decision-making and resource utilization in the Computing Continuum in the following ways:

Edge Intelligence: By deploying intelligent edge devices that can process data locally, scheduling algorithms can offload computation from centralized servers, reducing latency and improving response times. Edge intelligence enables real-time decision-making at the network edge, enhancing overall system efficiency.

Federated Learning: Implementing federated learning techniques allows distributed edge devices to collaboratively train machine learning models without sharing raw data. Scheduling algorithms can leverage federated learning to improve model accuracy while maintaining data privacy and security in the Computing Continuum.

Decentralized Decision-Making: Edge intelligence enables decentralized decision-making, where edge devices can autonomously make scheduling decisions based on local data and constraints. This distributed approach can optimize resource utilization and reduce dependency on centralized servers.

Adaptive Resource Allocation: By integrating edge intelligence into scheduling algorithms, resource allocation can be dynamically adjusted based on real-time data and environmental conditions. This adaptive approach ensures efficient resource utilization and responsiveness to changing workload demands.

Optimized Task Offloading: Leveraging edge intelligence for optimized task offloading decisions can improve system performance by determining the most suitable computing node for executing tasks based on factors like latency, bandwidth, and resource availability.

By incorporating edge intelligence and federated learning into scheduling algorithms, decision-making and resource utilization in the Computing Continuum can be enhanced, leading to improved efficiency, scalability, and responsiveness in distributed computing environments.